Quantifying the Effects of Recommendation Systems

2020/02/04 by Sunshine Chong, Andrés Abeliuk, Chong, Sunshine +1 · 1 citation
Computer Science · Business, Management and Accounting · #Recommender Systems and Techniques #Mobile Crowdsensing and Crowdsourcing #Consumer Market Behavior and Pricing

paper · pdf · doi:10.48550/arxiv.2002.01077

Abstract

Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedback loop in which user behavior becomes magnified in the algorithmic system. Popular items get recommended more frequently, creating the bias that affects and alters user preferences. In order to visualize and compare the different biases, we will analyze the effects of recommendation systems and quantify the inequalities resulting from them.

Citations

• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

Cited by

• Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation

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